Examples with KNNModel org.apache.ignite.ml.knn.models.KNNModel used on opensource projects

Example 6 with KNNModel

use of org.apache.ignite.ml.knn.models.KNNModel in project ignite by apache.

the class IgniteKNNClassificationBenchmark method test.

/**
 * {@inheritDoc}
 */
@Override
public boolean test(Map<Object, Object> ctx) throws Exception {
    // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
    // because we create ignite cache internally.
    IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), this.getClass().getSimpleName(), new Runnable() {

        /**
         * {@inheritDoc}
         */
        @Override
        public void run() {
            // IMPL NOTE originally taken from KNNClassificationExample.
            // Obtain shuffled dataset.
            LabeledDataset dataset = new Datasets().shuffleIris((int) (DataChanger.next()));
            // Random splitting of iris data as 70% train and 30% test datasets.
            LabeledDatasetTestTrainPair split = new LabeledDatasetTestTrainPair(dataset, 0.3);
            LabeledDataset test = split.test();
            LabeledDataset train = split.train();
            KNNModel knnMdl = new KNNModel(5, new EuclideanDistance(), KNNStrategy.SIMPLE, train);
            // Calculate predicted classes.
            for (int i = 0; i < test.rowSize() - 1; i++) knnMdl.apply(test.getRow(i).features());
        }
    });
    igniteThread.start();
    igniteThread.join();
    return true;
}

Example 7 with KNNModel

use of org.apache.ignite.ml.knn.models.KNNModel in project ignite by apache.

the class IgniteKNNRegressionBenchmark method test.

/**
 * {@inheritDoc}
 */
@Override
public boolean test(Map<Object, Object> ctx) throws Exception {
    // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
    // because we create ignite cache internally.
    IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), this.getClass().getSimpleName(), new Runnable() {

        /**
         * {@inheritDoc}
         */
        @Override
        public void run() {
            // IMPL NOTE originally taken from KNNRegressionExample.
            // Obtain shuffled dataset.
            LabeledDataset dataset = new Datasets().shuffleClearedMachines((int) (DataChanger.next()));
            // Normalize dataset
            Normalizer.normalizeWithMiniMax(dataset);
            // Random splitting of iris data as 80% train and 20% test datasets.
            LabeledDatasetTestTrainPair split = new LabeledDatasetTestTrainPair(dataset, 0.2);
            LabeledDataset test = split.test();
            LabeledDataset train = split.train();
            // Builds weighted kNN-regression with Manhattan Distance.
            KNNModel knnMdl = new KNNMultipleLinearRegression(7, new ManhattanDistance(), KNNStrategy.WEIGHTED, train);
            // Clone labels
            final double[] labels = test.labels();
            // Calculate predicted classes.
            for (int i = 0; i < test.rowSize() - 1; i++) knnMdl.apply(test.getRow(i).features());
        }
    });
    igniteThread.start();
    igniteThread.join();
    return true;
}

Example 8 with KNNModel

use of org.apache.ignite.ml.knn.models.KNNModel in project ignite by apache.

the class KNNClassificationTest method testBinaryClassificationTest.

/**
 */
public void testBinaryClassificationTest() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    double[][] mtx = new double[][] { { 1.0, 1.0 }, { 1.0, 2.0 }, { 2.0, 1.0 }, { -1.0, -1.0 }, { -1.0, -2.0 }, { -2.0, -1.0 } };
    double[] lbs = new double[] { 1.0, 1.0, 1.0, 2.0, 2.0, 2.0 };
    LabeledDataset training = new LabeledDataset(mtx, lbs);
    KNNModel knnMdl = new KNNModel(3, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
    Vector firstVector = new DenseLocalOnHeapVector(new double[] { 2.0, 2.0 });
    assertEquals(knnMdl.apply(firstVector), 1.0);
    Vector secondVector = new DenseLocalOnHeapVector(new double[] { -2.0, -2.0 });
    assertEquals(knnMdl.apply(secondVector), 2.0);
}

Example 9 with KNNModel

use of org.apache.ignite.ml.knn.models.KNNModel in project ignite by apache.

the class KNNClassificationTest method testBinaryClassificationFarPointsWithWeightedStrategy.

/**
 */
public void testBinaryClassificationFarPointsWithWeightedStrategy() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    double[][] mtx = new double[][] { { 10.0, 10.0 }, { 10.0, 20.0 }, { -1, -1 }, { -2, -2 }, { -1.0, -2.0 }, { -2.0, -1.0 } };
    double[] lbs = new double[] { 1.0, 1.0, 1.0, 2.0, 2.0, 2.0 };
    LabeledDataset training = new LabeledDataset(mtx, lbs);
    KNNModel knnMdl = new KNNModel(3, new EuclideanDistance(), KNNStrategy.WEIGHTED, training);
    Vector vector = new DenseLocalOnHeapVector(new double[] { -1.01, -1.01 });
    assertEquals(knnMdl.apply(vector), 1.0);
}

Example 10 with KNNModel

use of org.apache.ignite.ml.knn.models.KNNModel in project ignite by apache.

the class KNNClassificationTest method testPredictOnIrisDataset.

/**
 */
public void testPredictOnIrisDataset() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    LabeledDataset training = loadDatasetFromTxt(KNN_IRIS_TXT, false);
    KNNModel knnMdl = new KNNModel(7, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
    Vector vector = new DenseLocalOnHeapVector(new double[] { 5.15, 3.55, 1.45, 0.25 });
    assertEquals(knnMdl.apply(vector), 1.0);
}